Summary
In this chapter, we take advantage of particle swarm optimization to build fuzzy systems automatically for different kinds of problems by simply providing the objective function and the problem variables. Particle swarm optimization (PSO) is a technique used in complex problems, including multi-objective problems. Fuzzy systems are currently used in many kinds of applications, such as control, for their effectiveness and efficiency. However, these characteristics depend primarily on the model yield by human experts, which may or may not be optimized for the problem. To avoid dealing with inconsistent during the fuzzy systems generation, we used some known techniques, such as the WM method, to help evolving meaningful rules and clustering concepts to generate membership functions. Tests using three three-dimensional functions have been carried out and show that the evolutionary process is promising.
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Nedjah, N., Costa, S.O., de Macedo Mourelle, L., dos Santos Coelho, L., Mariani, V.C. (2011). PSO in Building Fuzzy Systems. In: Nedjah, N., dos Santos Coelho, L., Mariani, V.C., de Macedo Mourelle, L. (eds) Innovative Computing Methods and Their Applications to Engineering Problems. Studies in Computational Intelligence, vol 357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20958-1_3
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DOI: https://doi.org/10.1007/978-3-642-20958-1_3
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